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Proceedings Paper

Processing of visual information in the visual and object buffers of scene understanding based on network-symbolic models
Author(s): Gary Kuvich
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Paper Abstract

Modern computer vision systems suffer from the lack of human-like abilities to understand a visual scene, detect, unambiguously identify and recognize objects. Bottom-up fine-scale segmentation of image with grouping into regions can rarely be effective for real world images if applied to the whole image without having clear criteria of how further to combine obtained small distinctive neighbor regions into meaningful objects. On a certain scale, an object or a pattern can be perceived just as an object or a pattern rather than a set of neighboring regions. Therefore, a region of interest, where the object or pattern can be located, must be established first. Rough but wide peripheral human vision serves to this goal, while narrow but precise foveal vision analyzes and recognizes the object from the center of the region of interest after separating it from its background. Unlike the traditional computer vision models, biologically-inspired Network-Symbolic models convert image information into an 'understandable' Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction of the Visual and Object Buffers and the top-level knowledge system. This article describes the principles of data representation and processing of information in Visual and Object buffers that allow for scene analysis and understanding with identification and recognition of objects in the visual scene.

Paper Details

Date Published: 4 May 2006
PDF: 11 pages
Proc. SPIE 6246, Visual Information Processing XV, 624603 (4 May 2006); doi: 10.1117/12.662168
Show Author Affiliations
Gary Kuvich, Smart Computer Vision Systems (United States)


Published in SPIE Proceedings Vol. 6246:
Visual Information Processing XV
Zia-ur Rahman; Stephen E. Reichenbach; Mark A. Neifeld, Editor(s)

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